
Python Error Handling
This covers the error handling patterns you actually need in production Python code. It shows you how to validate inputs early with clear error messages, use Pydantic for complex validation, chain exceptions to preserve debug context, and handle partial failures in batch operations without aborting everything. The custom exception hierarchy examples are solid, and the batch processing pattern that tracks successes and failures separately will save you headaches when processing large datasets. Good practical coverage of turning messy failure scenarios into manageable, debuggable code.
- skills.sh installs
- 14.7K
- GitHub stars
- 42.4K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add wshobson/agents --skill python-error-handling --agent claude-codeInstalls into .claude/skills of the current project.
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Python Error Handling
Build robust Python applications with proper input validation, meaningful exceptions, and graceful failure handling. Good error handling makes debugging easier and systems more reliable.
When to Use This Skill
- Validating user input and API parameters
- Designing exception hierarchies for applications
- Handling partial failures in batch operations
- Converting external data to domain types
- Building user-friendly error messages
- Implementing fail-fast validation patterns
Core Concepts
1. Fail Fast
Validate inputs early, before expensive operations. Report all validation errors at once when possible.
2. Meaningful Exceptions
Use appropriate exception types with context. Messages should explain what failed, why, and how to fix it.
3. Partial Failures
In batch operations, don't let one failure abort everything. Track successes and failures separately.
4. Preserve Context
Chain exceptions to maintain the full error trail for debugging.
Quick Start
Sponsors
Details
- Repository
- github.com/wshobson/agents
- Categories
- Debugging, Automation & Workflows, Python
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